Medical Image Analysis UsingS4 Classes/Methods

An Update

Brandon WhitcherGlaxoSmithKline Clinical Imaging Centre

Hammersmith Hosptial, London

08 March 2011

Outline for Tonight

• Imaging biomarkers for oncology

• Magnetic resonance imaging (MRI)

• Dynamic contrast-enhanced MRI

• R package dcemriS4 with dependencies

• Live Demo!

Imaging Biomarkers for Oncology

Imaging Biomarkers for Oncology

• The characterization of perfusion in tissue is a useful endpointin clinical trials for drug development.

• Angiogenesis• Blood-brain-barrier integrity

• Imaging techniques may be used to assess perfusionnon-invasively.

• The characterization of diffusion in tissue has become apopular tool.

Magnetic Resonance Imaging

Magnetic Resonance Imaging

• A constant, homogeneous magnetic field (the B0 field) is usedto polarize spins.

• The exposure of nuclei to a radio frequency (RF) pulse (theB1 field) at the Larmor frequency causes the nuclei in thelower energy state to jump to the higher energy state.

• Macroscopic level: this causes net magnetization to spiral awayfrom the B0 field.

• After time, the magnetization vector becomes perpendicular tothe main B0 field.

• MR imaging is based on the relaxation that takes place afterthe RF pulse has stopped.

• It is repeated for many different levels of phase encoding tobuild up a matrix in k-space.

• A 2D Fourier transform is performed, resulting in a single slicefrom an MRI acquisition.

Dynamic Contrast-Enhanced MRI

Dynamic Contrast-Enhanced MRI

• The quantitative analysis of DCE-MRI involves fittingpharmacokinetic models to the concentration of a contrastagent over time.

• Gadolinium-based contrast agents are injected after severalbaseline scans.

• Using T1-weighted sequences, the reduction in T1 relaxationtime caused by the contrast agent is the dominant signalenhancement.

• T1-weighted kinetic curves have three major phases• the upslope• maximum enhancement• washout

Ct(t) = vpCp(t) + K trans [Cp(t) ⊗ exp(−kept)]

• dcemriS4 provides all stages of data analysis for DCE-MRIusing S4 nifti classes/methods.

Diffusion-weighted Imaging

R Package dcemriS4

• Title: A Package for Medical Image Analysis(S4 implementation)

• Description: A collection of routines and documentation thatallows one to perform voxel-wise quantitative analysis ofdynamic contrast-enhanced or diffusion-weighted MRI data.

• Depends: R (>= 2.6.0), grDevices, graphics, methods,oro.nifti, utils

• Suggests: bitops, minpack.lm, multicore, splines, XML

• License: BSD

• URL: http://www.dcemri.org/

Live demo!

Thank-you for your attention!